@@ -179,7 +179,7 @@ Trial configuration in kubeflow mode have the following configuration keys:
* gpuNum
* image
* Required key. In kubeflow mode, your trial program will be scheduled by Kubernetes to run in [Pod](https://kubernetes.io/docs/concepts/workloads/pods/pod/). This key is used to specify the Docker image used to create the pod where your trail program will run.
* We already build a docker image [msranni/nni](https://hub.docker.com/r/msranni/nni/) on [Docker Hub](https://hub.docker.com/). It contains NNI python packages, Node modules and javascript artifact files required to start experiment, and all of NNI dependencies. The docker file used to build this image can be found at [here](https://github.com/Microsoft/nni/tree/master/deployment/Dockerfile.build.base). You can either use this image directly in your config file, or build your own image based on it.
* We already build a docker image [msranni/nni](https://hub.docker.com/r/msranni/nni/) on [Docker Hub](https://hub.docker.com/). It contains NNI python packages, Node modules and javascript artifact files required to start experiment, and all of NNI dependencies. The docker file used to build this image can be found at [here](https://github.com/Microsoft/nni/tree/master/deployment/docker/Dockerfile). You can either use this image directly in your config file, or build your own image based on it.
* apiVersion
* Required key. The API version of your kubeflow.
* ps (optional). This config section is used to configure tensorflow parameter server role.
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@@ -196,4 +196,4 @@ Notice: In kubeflow mode, NNIManager will start a rest server and listen on a po
Once a trial job is completed, you can goto NNI WebUI's overview page (like http://localhost:8080/oview) to check trial's information.
Any problems when using NNI in kubeflow mode, plesae create issues on [NNI Github repo](https://github.com/Microsoft/nni).
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Any problems when using NNI in kubeflow mode, please create issues on [NNI Github repo](https://github.com/Microsoft/nni).
@@ -50,7 +50,7 @@ Compared with LocalMode and [RemoteMachineMode](RemoteMachineMode.md), trial con
* Required key. Should be positive number based on your trial program's memory requirement
* image
* Required key. In pai mode, your trial program will be scheduled by OpenPAI to run in [Docker container](https://www.docker.com/). This key is used to specify the Docker image used to create the container in which your trial will run.
* We already build a docker image [nnimsra/nni](https://hub.docker.com/r/msranni/nni/) on [Docker Hub](https://hub.docker.com/). It contains NNI python packages, Node modules and javascript artifact files required to start experiment, and all of NNI dependencies. The docker file used to build this image can be found at [here](https://github.com/Microsoft/nni/tree/master/deployment/Dockerfile.build.base). You can either use this image directly in your config file, or build your own image based on it.
* We already build a docker image [nnimsra/nni](https://hub.docker.com/r/msranni/nni/) on [Docker Hub](https://hub.docker.com/). It contains NNI python packages, Node modules and javascript artifact files required to start experiment, and all of NNI dependencies. The docker file used to build this image can be found at [here](https://github.com/Microsoft/nni/tree/master/deployment/docker/Dockerfile). You can either use this image directly in your config file, or build your own image based on it.
* dataDir
* Optional key. It specifies the HDFS data direcotry for trial to download data. The format should be something like hdfs://{your HDFS host}:9000/{your data directory}
* outputDir
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@@ -78,4 +78,4 @@ You can see there're three fils in output folder: stderr, stdout, and trial.log
If you also want to save trial's other output into HDFS, like model files, you can use environment variable `NNI_OUTPUT_DIR` in your trial code to save your own output files, and NNI SDK will copy all the files in `NNI_OUTPUT_DIR` from trial's container to HDFS.
Any problems when using NNI in pai mode, plesae create issues on [NNI github repo](https://github.com/Microsoft/nni).
Any problems when using NNI in pai mode, please create issues on [NNI github repo](https://github.com/Microsoft/nni).
[Autokeras](https://arxiv.org/abs/1806.10282) is a popular automl tools using Network Morphism. The basic idea of Autokeras is to use Bayesian Regression to estimate the metric of the Neural Network Architecture. Each time, it generates several child networks from father networks. Then it uses a naïve Bayesian regression estimate its metric value from history trained results of network and metric value pair. Next, it chooses the the child which has best estimated performance and adds it to the training queue. Inspired by its work and referring to its [code](https://github.com/jhfjhfj1/autokeras), we implement our Network Morphism method in our NNI platform.
If you want to know about network morphism trial usage, please check [Readme.md](../../../../../examples/trials/network-morphism/README.md) of the trial to get more detail.
If you want to know about network morphism trial usage, please check [Readme.md](../../../../../examples/trials/network_morphism/README.md) of the trial to get more detail.
